In [1]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import pickle
import glob
from ipywidgets import interact, interactive, fixed
from moviepy.editor import VideoFileClip
from IPython.display import HTML
%matplotlib inline
Camera Calibrations¶
In [2]:
# call cv2 undistor
#def undistort(image, matrix, dis):
# return cv2.undistort(image, matrix, dis, None, matrix)
nx = 9 # different than the course lecture
ny = 6
objpoints = []
imgpoints = []
# prepare object points, like (0,0,0), (1,0,0), ...
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)
img = cv2.imread('camera_cal/calibration3.jpg') # pick one
plt.imshow(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(gray)
#Finding chessboard corners (for an nx x ny board):
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# assume ret == True
imgpoints.append(corners)
objpoints.append(objp)
# Drawing detected corners on an image:
cv2.drawChessboardCorners(img, (nx, ny), corners, ret)
# Camera calibration, given object points, image points, and the shape of the grayscale image:
ret, matrix, dist, rvecs, tvecs = \
cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
# where dist is distortion coefficent
# matrix, camera matrix to transform 3D to 2D image points
# Undistorting a test image:
undistorted = cv2.undistort(img, matrix, dist, None, matrix)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,5))
ax1.set_title('Original Image', fontsize=20)
ax2.set_title('Undistorted Image', fontsize=20)
ax1.imshow(img)
ax2.imshow(undistorted)
plt.plot()
Out[2]:
In [3]:
# mkdir system call
%mkdir undistorted_folder
def save(dire, fname, img):
#print(fname)
fname = ''.join([dire,'undistorted_',fname])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cv2.imwrite(fname,img)
images = ['straight_lines1.jpg', 'straight_lines2.jpg',
'test1.jpg', 'test2.jpg','test3.jpg', 'test4.jpg', 'test5.jpg', 'test6.jpg']
for fname in images:
img = cv2.imread('test_images/'+fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
undistorted = cv2.undistort(img, matrix, dist, None, matrix) #undistort(img, matrix, dist)
save('undistorted_folder/', fname, undistorted)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=20)
ax2.imshow(undistorted)
ax2.set_title('Undistorted Image', fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [4]:
# Code taken from Udacity lecture
# Sobel Absolute Threshold
def abs_sobel_thresh(img, direction='x',
sobel_kernel=3, thresh=(0, 255)):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
scaled_sobel = None
# Sobel x
if direction == 'x':
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
abs_sobelx = np.absolute(sobelx)
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Sobel y
else:
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
abs_sobely = np.absolute(sobely)
scaled_sobel = np.uint8(255*abs_sobely/np.max(abs_sobely))
thresh_min = thresh[0]
thresh_max = thresh[1]
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
return sxbinary
In [5]:
# Sobel Magnitude Threshold
def mag_thresh(img, sobel_kernel=3,
mag_thresh=(0, 255)):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0,ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1,ksize=sobel_kernel)
magnitude = np.sqrt(np.square(sobelx)+np.square(sobely))
abs_magnitude = np.absolute(magnitude)
scaled_magnitude = np.uint8(255*abs_magnitude/np.max(abs_magnitude))
mag_binary = np.zeros_like(scaled_magnitude)
mag_binary[(scaled_magnitude >= mag_thresh[0]) & (scaled_magnitude <= mag_thresh[1])] = 1
return mag_binary
In [6]:
# Sobel Direction Threshold
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0,ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1,ksize=sobel_kernel)
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
arctan = np.arctan2(abs_sobely, abs_sobelx)
dir_binary = np.zeros_like(arctan)
dir_binary[(arctan >= thresh[0]) & (arctan <= thresh[1])] = 1
return dir_binary
In [7]:
def combined_sobel_gradient_thres(img, disp=False, s_thresh_min=150, s_thresh_max=255):
ksize = 3 # Sobel kernel size
gradx = abs_sobel_thresh(img, direction='x', sobel_kernel=ksize, thresh=(20, 100))
grady = abs_sobel_thresh(img, direction='y', sobel_kernel=ksize, thresh=(20, 100))
mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(20, 100))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.7, 1.4))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1))|((mag_binary == 1) & (dir_binary == 1))] = 1
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
combined_binary = np.zeros_like(combined)
combined_binary[(s_binary == 1) | (combined == 1)] = 1
fs = 30 # font size
if disp == True:
f, (ax1, ax2) = plt.subplots(2, 2, figsize=(40,20))
ax1[0].set_title('Original Image', fontsize=20)
ax1[0].imshow(img)
ax1[1].set_title('Combined gradx, grady, mag, dir', fontsize=fs)
ax1[1].imshow(combined, cmap='gray')
ax2[0].set_title('Color Thresholding', fontsize=fs)
ax2[0].imshow(s_binary, cmap='gray')
ax2[1].set_title('Combined S Binary', fontsize=fs)
ax2[1].imshow(combined_binary, cmap='gray')
return combined_binary, img, combined, s_binary
In [8]:
# check the plots
"""
img = 'undistorted_folder/undistorted_test6.jpg'
img = cv2.imread(img)
combined_binary, _, _, _ = combined_sobel_gradient_thres(img, disp=True)
img = 'undistorted_folder/undistorted_test2.jpg'
img = cv2.imread(img)
combined_binary, _, _, _ = combined_sobel_gradient_thres(img, disp=True)
img = 'undistorted_folder/undistorted_test5.jpg'
img = cv2.imread(img)
combined_binary, _, _, _ = combined_sobel_gradient_thres(img, disp=True)
"""
#img = 'undistorted_folder/undistorted_straight_lines1.jpg'
img = 'undistorted_folder/undistorted_test1.jpg'
img = cv2.imread(img)
combined_binary, img_color, combined_1, s_binary = combined_sobel_gradient_thres(img, disp=True)
In [ ]:
In [9]:
def transform_image_warp(img, nx, ny):
offset = 100 # offset for dst points
#type(img)
# Grab the image shape
img_size = (img.shape[1], img.shape[0])
#print(imgsz)
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST)
return warped, M
def display_transformed_image(combined_binary, warped_img):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(combined_binary, cmap='gray')
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(warped_img,cmap='gray')
ax2.set_title('Warped Image', fontsize=30)
plt.show()
In [10]:
#warped, M = transform_image(combined_binary, src, dst, matrix)
warped_img, M = transform_image_warp(combined_binary, nx, ny)
display_transformed_image(combined_binary, warped_img)
color_warped_img, color_M = transform_image_warp(img_color, nx, ny)
display_transformed_image(img_color, color_warped_img)
"""
img1 = 'undistorted_folder/undistorted_test5.jpg'
img1 = cv2.imread(img1)
combined_binary_1, _, _, _ = combined_sobel_gradient_thres(img1, disp=False)
warped_img_1, M_1 = transform_image_warp(combined_binary_1, nx, ny)
display_transformed_image(combined_binary_1, warped_img_1)
"""
Out[10]:
In [11]:
# Locating the Lanes
def locating_lanes(binary_warped, nwindows=9, margin=100, minpix=50):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
## Create an image to draw on and an image to show the selection window
##out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
return left_fit, right_fit,left_lane_inds, right_lane_inds, nonzerox, nonzeroy, histogram
In [12]:
def display_lanes(left_fit, right_fit, left_lane_inds,
right_lane_inds, binary_warped,
nonzerox, nonzeroy, histogram, margin=50):
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [100, 200, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0, 255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0, 255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(binary_warped)
#plt.imshow(out_img)
#plt.imshow(result)
plt.plot(left_fitx, ploty, color='red')
plt.plot(right_fitx, ploty, color='red')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.show()
In [13]:
left_fit, right_fit, left_lane_inds, right_lane_inds,\
nonzerox, nonzeroy, histogram = locating_lanes(warped_img)
display_lanes(left_fit, right_fit, left_lane_inds, \
right_lane_inds, warped_img, nonzerox, nonzeroy, histogram)
In [14]:
print('......')
plt.plot(histogram)
plt.show()
Radius of Curvature & Distance from Lane Center¶
In [15]:
def radius_curvature_center(binary_img, left_fit, right_fit):
ym_per_pix = 30/720 #3.048/100 # meters per pixel in y dimension
xm_per_pix = 3.7/700 #3.7/380 #3.7/700 # meters per pixel in x dimension
h = binary_img.shape[0]
ploty = np.linspace(0, h-1, h)
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
y_eval = np.max(ploty)
# follow udacity lecture formulat
left_fit_cr = np.polyfit(ploty*ym_per_pix, left_fitx*xm_per_pix, 2)
left_curv = ((1 + (2*left_fit_cr[0] *y_eval*ym_per_pix \
+ left_fit_cr[1])**2) **1.5) / np.absolute(2*left_fit_cr[0])
right_fit_cr = np.polyfit(ploty*ym_per_pix, right_fitx*xm_per_pix, 2)
right_curv = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix \
+ right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Calculate vehicle center
#left_lane and right lane bottom in pixels
left_lane_bottom = (left_fit[0]*y_eval)**2 + left_fit[0]*y_eval + left_fit[2]
right_lane_bottom = (right_fit[0]*y_eval)**2 + right_fit[0]*y_eval + right_fit[2]
# Lane center as mid of left and right lane bottom
lane_center = (left_lane_bottom + right_lane_bottom)/2.
center_image = binary_img.shape[1]/2 #640
center_dist = (lane_center - center_image) * xm_per_pix #Convert to meters
return left_curv, right_curv, center_dist
In [16]:
left_curv, right_curv, center = radius_curvature_center(warped_img, left_fit, right_fit)
In [17]:
def draw_on_image(undist, warped_img, left_fit, right_fit, M, \
left_curv, right_curv, center, show_values = False):
ploty = np.linspace(0, warped_img.shape[0]-1, warped_img.shape[0])
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image and draw the lines on
warp_zero = np.zeros_like(warped_img).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lanes the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
M_inverse = np.linalg.inv(M)
# Warp the blank back to original image space using inverse perspective matrix
unwarped = cv2.warpPerspective(color_warp, M_inverse, \
(undist.shape[1], img.shape[0]))
result = cv2.addWeighted(undist, 1, unwarped, 0.3, 0)
cv2.putText(result, 'left curvature: {:.2f} m'.format(left_curv), \
(50, 50), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 2)
cv2.putText(result, 'right curvature: {:.2f} m'.format(right_curv), \
(50, 100), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 2)
lr = ''
if(center > 0): lr = 'right'
elif (center<0): lr = 'left'
else: lr = 'center'
cv2.putText(result, 'vehicle is {:.2f}m {}'.format(abs(center), lr), \
(50, 150), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 2)
if show_values == True:
fig, ax = plt.subplots(figsize=(15, 8))
ax.imshow(result)
return result
In [18]:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
draw_on_image(img, warped_img, left_fit, right_fit, M, left_curv, right_curv, center, True)
Out[18]:
Pipeline¶
In [19]:
# for pipeline based on time-series images from the video
def locating_lanes_with_prev(left_fit, right_fit, binary_warped):
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 80
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin))
& (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin))
& (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit_ = []
right_fit_ = []
if len(leftx) != 0:
left_fit_ = np.polyfit(lefty, leftx, 2)
if len(rightx) != 0:
right_fit_ = np.polyfit(righty, rightx, 2)
return left_fit_, right_fit_
def is_good_lane(lane_class):
status = True
if lane_class.nlen_in > 0:
left_diff = np.sum(np.absolute(lane_class.prev_left_lanes - lane_class.left_fit))
right_diff = np.sum(np.absolute(lane_class.prev_right_lanes - lane_class.right_fit))
thres_margin = 50
diff_threshold = thres_margin * len(lane_class.prev_right_lanes)
if left_diff > diff_threshold or right_diff > diff_threshold:
print(diff_threshold, left_diff, right_diff)
status = False
else:
status = True
return status
Out[19]:
In [20]:
# create a global data structure to store the time-series images
class Laneline():
def __init__(self):
self.last_left = None
self.last_right = None
self.left_fit = None
self.right_fit = None
self.count = 0
self.histograms = None
self.prev_left_lanes = []
self.prev_right_lanes = []
self.bad_count = 0
self.nlen = 50 # buffer size
self.nlen_in = 0
#self.current_left_lanes = None
#self.current_right_lane = None
In [21]:
lane = Laneline()
def find_lanes(img):
img = cv2.undistort(img, matrix, dist, None, matrix)
combined_binary, _, _, _ = combined_sobel_gradient_thres(img)
warped_img, M = transform_image_warp(combined_binary, nx, ny)
#print('lane counter --> ', lane.counter)
if lane.count == 0:
lane.left_fit, lane.right_fit, left_lane_inds,\
right_lane_inds, nonzerox, nonzeroy, lane.histograms = locating_lanes(warped_img)
#lane.count += 1
else:
lane.left_fit, lane.right_fit = \
locating_lanes_with_prev(lane.left_fit, lane.right_fit, warped_img)
#print(lane.left_fit, lane.right_fit)
#return warped_img, lane.left_fit, lane.right_fit, M
#status, delta_l, delta_s = is_good_lane(lane.left_fit, lane.right_fit)
status = is_good_lane(lane)
print(status)
#, delta_l, delta_s)
if status == True:
bad_count = 0
lane.prev_left_lanes.append(lane.left_fit)
lane.prev_right_lanes.append(lane.right_fit)
nlen = lane.nlen
lane.nlen_in += 1
if lane.nlen_in > nlen: #len(lane.prev_right_lanes) > nlen:
lane.prev_right_lanes = lane.prev_right_lanes[-nlen::]
#self.nlen_in = nlen
#if len(lane.prev_left_lanes) > nlen:
lane.prev_left_lanes = lane.prev_left_lanes[-nlen::]
lane.nlen_in = nlen
#print(lane.prev_right_lanes)
#print(len(lane.prev_left_lanes))#, lane.prev_right_lanes)
else:
lane.bad_count += 1
right_ls = lane.prev_right_lanes[0]
len_rls = len(lane.prev_right_lanes)
for i in range(1, len_rls):
right_ls = np.add(right_ls, lane.prev_right_lanes[i])
left_ls = lane.prev_left_lanes[0]
len_lls = len(lane.prev_left_lanes)
for i in range(1, len_lls):
left_ls = np.add(left_ls, lane.prev_left_lanes[i])
lane.left_fit = left_ls / len_lls
lane.right_fit = right_ls / len_rls
if lane.bad_count >= 30:
print('clear bad lanes')
lane.bad_count = 0
lane.prev_left_lanes = []
lane.prev_right_lanes = []
lane.nlen_in = 0
return warped_img, lane.left_fit, lane.right_fit, M
In [22]:
def pipeline(img, show_values=False):
warped_img, left_fit, right_fit, M = find_lanes(img)
left_curvature, right_curvature, center = \
radius_curvature_center(warped_img, left_fit, right_fit)
return draw_on_image(img, warped_img, left_fit,\
right_fit, M, left_curvature, right_curvature, center, show_values)
images = ['straight_lines1.jpg', 'straight_lines2.jpg',\
'test1.jpg', 'test2.jpg','test3.jpg', 'test4.jpg', \
'test5.jpg', 'test6.jpg']
for fname in images:
img = cv2.imread('test_images/'+fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
pipeline(img, True)
Video Generations¶
In [23]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
lane = Laneline()
def process_video(img):
return pipeline(img)
%mkdir videos
In [24]:
output_file = 'videos/project_video_output.mp4'
clip = VideoFileClip("project_video.mp4")
project_video_output = clip.fl_image(process_video)
%time project_video_output.write_videofile(output_file, audio=False)
In [25]:
HTML("""<video width="900" height="400" controls><source src="{0}"></video>""".format(output_file))
Out[25]:
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